Settings & annotation sources

The annotated variants are based on the following underlying tools and knowledge resources:

  • VEP - Variant Effect Predictor (v94)
  • GENCODE - high quality reference gene annotation and experimental validation (release 28)
  • dbNSFP - Database of non-synonymous functional predictions (v3.5 (August 2017))
  • Pfam - Collection of protein families/domains (v32 (September 2018))
  • TCGA - The Cancer Genome Atlas - somatic mutations (release 13 (September 27th 2018))
  • ICGC-PCAWG - ICGC-Pancancer Analysis of Whole Genomes - somatic mutations (release 27 (April 2018))
  • UniProtKB - Comprehensive resource of protein sequence and functional information (release 2018_10)
  • CORUM - The comprehensive resource of mammalian protein complexes (release 3.0 (20180903))
  • gnomAD - Germline variant frequencies exome-wide (r2.0 (October 2017))
  • COSMIC - Catalogue of somatic mutations in cancer (86/81)
  • dbSNP - Database of short genetic variants (151/150)
  • 1000Genomes - Germline variant frequencies genome-wide (phase 3 (20130502))
  • DisGenet - Database of gene-disease associations (v5.0 (May 2017))
  • IntOGen - IntOGen catalog of predicted cancer driver mutations (release 2016.05)
  • DoCM - Database of curated mutations (release 3.2)
  • CancerHotspots - A resource for statistically significant mutations in cancer (2017)
  • ClinVar - Database of genomic variants of clinical significance (release 20181101)
  • CancerMine - Literature-mined database of tumor suppressor genes/proto-oncogenes (v6 (20181108))
  • DiseaseOntology - Standardized ontology for human disease (20181102)
  • OncoScore - Literature-based ranking of gene-cancer association (20180330)
  • DGIdb - Database of targeted antineoplastic drugs (v3.0.2 (January 2018))
  • ChEMBL - Manually curated database of bioactive molecules (v24.1 (June 2018))
  • KEGG - Knowledge base on the molecular interaction, reaction and relation networks (20181024)
  • CIViC - Clinical interpretations of variants in cancer (November 12th 2018)
  • CBMDB - Cancer Biomarkers database (January 17th 2018)

The report is generated with PCGR version dev, using the following key settings:

  • Genome assembly: grch37
  • Tumor type: Colorectal_Cancer_NOS
  • Sequencing mode input (VCF): Tumor-only (no matching control)
  • Coding target size (VCF): 36 Mb
  • Minimum sequencing depth (DP) tumor (VCF): 0
  • Minimum allelic fraction (AF) tumor (VCF): 0
  • Minimum sequencing depth (DP) normal (VCF): 0
  • Maximum allelic fraction (AF) normal (VCF): 1
  • Tier system (VCF): pcgr_acmg
  • MSI prediction: ON
  • Mutational burden estimation: ON
  • Mutational signatures estimation: ON
  • Report theme (Bootstrap): default

Main results

Somatic SNVs/InDels

Tumor-only filtering statistics

Germline variant filter settings:

  • Exclude 1000 Genomes Project variants (global): MAF >= 0.01
  • Exclude 1000 Genomes Project variants (EUR): MAF >= 0.01
  • Exclude 1000 Genomes Project variants (AMR): MAF >= 0.01
  • Exclude 1000 Genomes Project variants (AFR): MAF >= 0.01
  • Exclude 1000 Genomes Project variants (EAS): MAF >= 0.01
  • Exclude 1000 Genomes Project variants (SAS): MAF >= 0.01
  • Exclude gnomAD variants (global): MAF >= 0.01
  • Exclude gnomAD variants (NFE): MAF >= 0.01
  • Exclude gnomAD variants (AMR): MAF >= 0.01
  • Exclude gnomAD variants (AFR): MAF >= 0.01
  • Exclude gnomAD variants (EAS): MAF >= 0.01
  • Exclude gnomAD variants (SAS): MAF >= 0.01
  • Exclude gnomAD variants (FIN): MAF >= 0.01
  • Exclude gnomAD variants (OTH): MAF >= 0.01
  • Exclude nonclinically associated dbSNP variants: TRUE
    • Keep known TCGA variants: TRUE
    • Minimum TCGA variant recurrence: 2
  • Exclude non-coding variants: TRUE

Total number of (unfiltered, non-rejected) calls: 3316
Number of calls remaining after successive filtering steps:

  1. Exclusion of variants found in 1000 Genomes Project: 3310 ( 99.82 % of unfiltered calls)
  2. Exclusion of variants found in gnomAD: 3308 ( 99.76 % of unfiltered calls)
  3. Exclusion of nonclinically associated dbSNP variants (FILTER = ON): 2616 ( 78.89 % of unfiltered calls)
  4. Exclusion of non-coding variants (FILTER = ON): 1665 ( 50.21 % of unfiltered calls)


IMPORTANT NOTE: All SNV/InDel analyses below are considering the filtered callset only, n = 1665

Tier & variant statistics

  • Number of SNVs: 1217
  • Number of InDels: 448
  • Number of protein-coding variants: 1665

The prioritization of SNV/InDels is here done according to a four-tiered structure, adopting the joint consensus recommendation by the ACMG (Li et al. 2017).

  • Tier 1 - variants of strong clinical significance: 1
  • Tier 2 - variants of potential clinical significance: 0
  • Tier 3 - variants of unknown clinical significance: 150
  • Tier 4 - other coding variants: 1514
  • Noncoding variants: 0



Global distribution - allelic support


Global variant browser

The table below permits filtering of the total SNV/InDel set by various criteria.

NOTE 1: The filtering applies to this table only, and not to the tier-specific tables below.

NOTE 2: Filtering on sequencing depth/allelic fraction depends on input specified by user (VCF INFO tags).





Tier 1 - Variants of strong clinical significance



Predictive biomarkers


The table below lists all variant-evidence item associations:



Prognostic biomarkers


The table below lists all variant-evidence item associations:



Diagnostic biomarkers


No variant-evidence item associations found.



Tier 2 - Variants of potential clinical significance

  • Tier 2 considers evidence items of i) strong evidence levels (A & B) in other tumor types, and ii) weak evidence levels (C, D & E) in the query tumor type (Colorectal_Cancer_NOS). Using the database for clinical interpretations of variants in cancer (CIViC) and Cancer Biomarkers database, a total of 0 unique, somatic variants were found in the tumor sample:
    • Tier 2 - Predictive/Therapeutic: 0 evidence items
    • Tier 2 - Prognostic: 0 evidence items
    • Tier 2 - Diagnostic: 0 evidence items



Predictive biomarkers


No variant-evidence item associations found.



Prognostic biomarkers


No variant-evidence item associations found.



Diagnostic biomarkers


No variant-evidence item associations found.



Tier 3 - Variants of unknown clinical significance

  • A total of 150 unique, somatic variant(s) in the tumor sample are of unknown clinical significance, as found within known proto-oncogenes or tumor suppressor genes.

Tumor suppressor gene mutations


The table below lists all variants:



Proto-oncogene mutations


The table below lists all variants:



Tier 4 - Other coding mutations

  • A total of 1514 unique, coding somatic variant(s) are also found in the tumor sample.





Noncoding mutations

  • A total of 0 unique, somatic variant(s) are also found in the tumor sample.



Somatic CNAs


Segments - amplifications and homozygous deletions

The following user-defined thresholds determine copy number aberrations shown here:

  • Copy number amplifications: Log(2) ratio >= 0.8
  • Homozygous deletions: Log(2) ratio <= -0.8

A total of 75 unfiltered aberration segments satisfied the above criteria.


A total of copy number segments satisfy the current filtering criteria.




Proto-oncogenes subject to copy number amplifications


A total of 0 proto-oncogenes are covered (i.e. transcript overlapping >= 50%) by genomic segments subject to amplifications.



Tumor suppressor genes subject to homozygous deletions


A total of 0 tumor suppressor genes are covered (i.e. transcript overlapping >= 50%) by genomic segments subject to homozygous deletions.



Copy number aberrations as biomarkers

A total of 0 aberrations are associated with clinical evidence items in the database for clinical interpretations of variants in cancer, CIViC, with the following number of evidence items:

  • Predictive: 0 evidence items
  • Prognostic: 0 evidence items
  • Diagnostic: 0 evidence items



Aberrations of strong clinical significance

Predictive biomarkers


No variant-evidence item associations found.



Prognostic biomarkers


No variant-evidence item associations found.



Diagnostic biomarkers


No variant-evidence item associations found.



Aberrations of potential clinical significance

Predictive biomarkers


No variant-evidence item associations found.



Prognostic biomarkers


No variant-evidence item associations found.



Diagnostic biomarkers


No variant-evidence item associations found.



References

Alexandrov, Ludmil B, Serena Nik-Zainal, David C Wedge, Samuel A J R Aparicio, Sam Behjati, Andrew V Biankin, Graham R Bignell, et al. 2013. “Signatures of Mutational Processes in Human Cancer.” Nature 500 (7463): 415–21.

Alexandrov, Ludmil B, Serena Nik-Zainal, David C Wedge, Peter J Campbell, and Michael R Stratton. 2013. “Deciphering Signatures of Mutational Processes Operative in Human Cancer.” Cell Rep. 3 (1): 246–59.

Cortes-Ciriano, Isidro, Sejoon Lee, Woong-Yang Park, Tae-Min Kim, and Peter J Park. 2017. “A Molecular Portrait of Microsatellite Instability Across Multiple Cancers.” Nat. Commun. 8: 15180.

Dong, Fei, Phani K Davineni, Brooke E Howitt, and Andrew H Beck. 2016. “A BRCA1/2 Mutational Signature and Survival in Ovarian High-Grade Serous Carcinoma.” Cancer Epidemiol. Biomarkers Prev. 25 (11): 1511–6.

Kim, Jaegil, Kent W Mouw, Paz Polak, Lior Z Braunstein, Atanas Kamburov, Grace Tiao, David J Kwiatkowski, et al. 2016. “Somatic ERCC2 Mutations Are Associated with a Distinct Genomic Signature in Urothelial Tumors.” Nat. Genet. 48 (6): 600–606.

Li, Marilyn M, Michael Datto, Eric J Duncavage, Shashikant Kulkarni, Neal I Lindeman, Somak Roy, Apostolia M Tsimberidou, et al. 2017. “Standards and Guidelines for the Interpretation and Reporting of Sequence Variants in Cancer: A Joint Consensus Recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists.” J. Mol. Diagn. 19 (1): 4–23.

Rosenthal, Rachel, Nicholas McGranahan, Javier Herrero, Barry S Taylor, and Charles Swanton. 2016. “DeconstructSigs: Delineating Mutational Processes in Single Tumors Distinguishes DNA Repair Deficiencies and Patterns of Carcinoma Evolution.” Genome Biol. 17 (1): 31.

Secrier, Maria, Xiaodun Li, Nadeera de Silva, Matthew D Eldridge, Gianmarco Contino, Jan Bornschein, Shona MacRae, et al. 2016. “Mutational Signatures in Esophageal Adenocarcinoma Define Etiologically Distinct Subgroups with Therapeutic Relevance.” Nat. Genet. 48 (10): 1131–41.




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